Assessing the impact of climate-related risks on Canadian real estate investment trusts: insights and implications for investors
Bibliographic record
Abstract
Purpose We evaluate how physical climate risks influence the operational and financial performance of Canadian real estate investment trusts (REITs). We focus on natural disasters and show how systematic climate risk assessment can strengthen investment and management decisions. Design/methodology/approach Using the multi-hazard exposure (MHE) average index (Duprey et al., 2021), we match a century of disaster data to 1,658 Canadian Forward Sortation Areas (FSAs). Panel regressions link portfolio-level MHE exposure to operating metrics (rental revenue, operating expenses, net operating income (NOI) and adjusted funds from operations (FFO)) and market metrics (abnormal return and beta). Findings MHE exposure reduces rental revenue and is unexpectedly associated with lower operating expenses, suggesting strategic cost management, while market pricing already embeds the risk. Impacts differ by property type: multifamily and self-storage assets are most exposed; hotels and retail assets show relative resilience. Practical implications Investors and regulators should embed forward-looking climate risk metrics in capital allocation, insurance and disclosure. Recent instruments such as OSFI Guideline B-15 and the Canadian Securities Administrators' climate disclosure rule illustrate viable policy levers. Originality/value We unite a granular multi-hazard index with REIT-level financial and operating data, providing the first evidence for Canada and demonstrating how adaptive management strategies can mitigate location-specific climate exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".